[PhD] Optimized super-resolution adapted to shooting conditions

Where : in La Rochelle Duration: 3 years (fixed-term contract) Contract start date: October 1, 2026 Salary: ~ 2300 € / month (gross) Workplace: L3i Laboratory, La Rochelle University Supervisors: Petra Gomez-Krämer (L3i) and Chaker Larabi (XLIM, Université de Poitiers) Key words: Video processing, image quality enhancement, super-resolution Only students…

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[PhD] Multi-modal data synchronization for speech and gesture analysis

Application deadline : April 18, 2026 Final results : June, 2026 Duration : 3 years starting from October 2026 Place : Université de Lille – CRIStAL, Villeneuve d’Ascq 59655, France Supervisors: • Mohamed Daoudi, Professor (mohamed.daoudi@univ-lille.fr) • Deise Santana Maia, Assistant Professor (deise.santanamaia@univ-lille.fr) This PhD thesis proposal falls within the scope of the research…

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[PhD] Explainable Deep Learning for Early Identification of Neurodevelopmental Alterations through the Analysis of Cardiac Autonomic Regulation

Starting date: October 2026Application deadline date: March 20th, 2026Final decision date: June 4th, 2026Contact: hugues.patural@univ-st-etienne.fr, olivier.alata@univ-st-etienne.fr Position Overview This PhD project corresponds to a fully funded 3-year doctoral position within the framework of the Doctoral School EDSIS. The thesis will be conducted at Université Jean Monnet, Saint-Étienne, France, with an…

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[PhD] Détection de signaux faibles en contexte volcanique tropical : Apprentissage profond et traitement avancé du bruit électromagnétique

Mots clés : Estimation robuste du signal MT, deep Learning interprétable, traitement adaptatifen contexte bruité, séries temporelles multivariées, suivi électromagnétique des volcans Résumé : L’exploitation des données magnétotelluriques (MT) en contexte fortement bruité reste un défi scientifique majeur, en particulier pour la détection de signaux faibles associés aux dynamiques profondes…

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[StageM2+PhD] Hyperspectral Foundation Models

The foundation model (FM) paradigm is undoubtedly a major breakthrough in Machine Learning (ML) for Artificial Intelligence (AI). An FM is a large-scale neural network pre-trained with self-supervision on a vast unannotated dataset and designed to perform downstream tasks with minimal fine-tuning on small annotated datasets. While FMs have made…

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